Do masked orthographic neighbor primes facilitate or inhibit the processing of Kanji compound words?
Bibliographic record
Abstract
In the masked priming paradigm, when a word target is primed by a higher frequency neighbor (e.g., blue-BLUR), lexical decision latencies are slower than when the same word is primed by an unrelated word of equivalent frequency (e.g., care-BLUR). This inhibitory neighbor priming effect (e.g., Davis & Lupker, 2006; Segui & Grainger, 1990) is taken as evidence for the lexical competition process that is an important component of localist activation-based models of visual word recognition (Davis, 2003; Grainger & Jacobs, 1996; McClelland & Rumelhart, 1981). The present research looked for evidence of an inhibitory neighbor priming effect using words written in Japanese Kanji, a logographic, nonalphabetic script. In 4 experiments (Experiments 1A, 1B, 3A, and 3B), inhibitory neighbor priming effects were observed for low-frequency targets primed by higher frequency Kanji word neighbors ([symbol in text]). In contrast, there was a significant facilitation effect when targets were primed by Kanji nonword neighbors ([symbols in text]; Experiments 2 and 3). Significant facilitation was also observed when targets were primed by single constituent Kanji characters ([symbols in text]; Experiment 4). Taken together, these results suggest that lexical competition plays a role in the recognition of Kanji words, just as it does for words in alphabetic languages. However, in Kanji, and likely in other logographic languages, the effect of lexical competition appears to be counteracted by facilitory morphological priming due to the repetition of a morphological unit in the prime and target (i.e., in Kanji, each character represents a morpheme).
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".